A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Density destructors simplify complex PDFs to maximize entropy, linking to information theory.
Optimizes kernel density ratios for better predictions and information measures.
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined…
We show a general relation between the spatially disjoint product of probability density functions and the sum of their Fisher information metric tensors. We then utilise this result to give a method for constructing the probability density functions for an arbitrary Riemannian Fisher information metric tensor. We note…
New bounds on generalization error using information density moments.
We present a novel synthesis of Fisher information and asset pricing theory that yields a practical method for reconstructing the probability density implicit in security prices. The Fisher information approach to these inverse problems transforms the search for a probability density into the solution of a differential…
We address the following problem: given two smooth densities on a manifold, find an optimal diffeomorphism that transforms one density into the other. Our framework builds on connections between the Fisher-Rao information metric on the space of probability densities and right-invariant metrics on the infinite-dimension…
New bounds on learning algorithm generalization error derived using information density.
The paper analyzes how the one-dimensional Wasserstein distance captures pointwise density differences in finite samples.
Estimates copula density for complex data distributions.
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" co…
Unified framework for robust, stable, and efficient density ratio estimation.
Featurization improves density ratio estimation for complex data.
Method scales up ML science by measuring multiple molecules at once.
Estimates expected information gain using density approximations and dimension reduction.
Develops spherical density-equalizing maps for closed surfaces.
KSG mutual information estimator, which is based on the distances of each sample to its k-th nearest neighbor, is widely used to estimate mutual information between two continuous random variables. Existing work has analyzed the convergence rate of this estimator for random variables whose densities are bounded away fr…
Classical probability distributions on sets of sequences can be modeled using quantum states. Here, we do so with a quantum state that is pure and entangled. Because it is entangled, the reduced densities that describe subsystems also carry information about the complementary subsystem. This is in contrast to the class…
This paper provides a neural approach to represent option implied information.
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly i…
We study the problem of finding probability densities that match given European call option prices. To allow prior information about such a density to be taken into account, we generalise the algorithm presented in Neri and Schneider (2011) to find the maximum entropy density of an asset price to the relative entropy c…
We build on the work in Fackler and King 1990, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time de…
New statistical framework for coresets in density estimation.
New MI bounds improve estimation in deep generative models.
Paper proposes a new method for estimating conditional densities using logistic regressions.
We consider the problem of sampling from a strongly log-concave density in , and prove an information theoretic lower bound on the number of stochastic gradient queries of the log density needed. Several popular sampling algorithms (including many Markov chain Monte Carlo methods) operate by using stochas…
TRE improves density-ratio estimation for highly dissimilar densities.
Survey of linking information geometry and optimal transport.
Optimizes noisy IS with better proposal densities.
A new method improves model generalization by recognizing representations.
We construct an infinite-dimensional information manifold based on exponential Orlicz spaces without using the notion of exponential convergence. We then show that convex mixtures of probability densities lie on the same connected component of this manifold, and characterize the class of densities for which this mixtur…
Quantum probability theory reveals hidden structure in joint probability distributions.
In this paper we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible allowing us posterior density estimation and posterior inference for high quantiles. …
A novel method compares 3D point clouds using information geometry.
A new method improves density ratio estimation efficiency and accuracy.
A deep learning method for probabilistic weather forecasting.
DPS uses PINNs to estimate drift in diffusion models for sampling.
Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which t…
A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure t…
Proposes VAE-KRnet for density estimation and variational Bayes.
This work integrates differentiation and integration in Physics-Informed Neural Networks.
Recent works investigated the generalization properties in deep neural networks (DNNs) by studying the Information Bottleneck in DNNs. However, the mea- surement of the mutual information (MI) is often inaccurate due to the density estimation. To address this issue, we propose to measure the dependency instead of MI be…
The paper proposes a method for interpretable mixture density estimation using a tree structure.
This work tackles sequential data learning challenges by improving neural network robustness to non-iid distribution shifts.
The geometric approach to optimal transport and information theory has triggered the interpretation of probability densities as an infinite-dimensional Riemannian manifold. The most studied Riemannian structures are Otto's metric, yielding the -Wasserstein distance of optimal mass transport, and the Fisher--Rao me…
Neural net reconstructs dark matter density from halo velocities.
Introduces new info-geometric structure for dynamics on graphs and hypergraphs.
New method improves sample-efficiency in neural posterior estimation using simulator gradients.